Data Science in Finance

Managing Credit Card Default Risk


When a bank employee or lender logs in, they see a data dashboard showing columns and cells with key aspects that they would like to monitor. The dashboard is also capable of showing insights and trends in various graph formats. A user can also search and look at specific data associated with someone applying for a loan and his or her loan application to determine if they should get approved.

According to finance companies, their services aim to predict risk in lending (credit default rates) or identify anomalies in payment transactions for fraud detection. For example:

  • They may lend money to customers in the form of loans or credit cards and growing their business means increasing the value and number of such loans. The bank may need a scalable strategy to predict the likelihood (risk) of default among large numbers of applicants.
  • The bank’s loan managers might use the platform to gain insights on the risk of loan defaults for new customers by means of a dashboard.
  • The AI platforms are trained using historical loan repayment records and other data like social media data to coax out patterns that might lead to a customer defaulting on credit card payments. The platform can also clean and parse the raw data although users can also use third party data cleaning tools.
  • Loan managers can review the applications that have a high risk of default thereby speeding up the loan approval process.
  • The institutions use a predictive analytics platform to predict the risk of default for new borrowers by analyzing historical data about existing borrowers’ default rates. By integrating these predictive models into their loan-approval the bank could potentially expand their loan portfolios while simultaneously managing the risk involved.
  • To previously attempt to predict default rates, the team can use the information provided by the customer and additional data, like rent and utility payment histories gathered from the credit or background checks, as inputs for their built-in-house machine learning models.
  • Since the team was building and testing the machine learning predictive models manually, this process often took months, facing several deployment delays.
  • After the integration of their platform, the team was successfully able to Identify the customers in high-risk and highly competitive markets, detect anomalies in customer transactions that might be fraudulent, and predict the likelihood of default for loan applicants.

Modeling Customer Lifetime Value

Certain finance institutions offer software that can help data science teams to develop predictive models in fields including industry banking, healthcare and automotive.

They claim to be using AI for predictive analytics in areas like pricing optimization, predicting customer lifetime value and fraud detection. Their use-case on predicting customer lifetime value states that banks might use their platform to:

  • Predict the lifetime value of a customer based on their historical transaction data.
  • Identify customers with high long-term values and prompt marketing options based on the type of customer.
  • Identify the ‘profiles’ for ideal long-term customers which can then be used to predict if a new customer might fall under this category.
  • Help direct the bank’s cost and effort towards customers that might continue working with the bank in the future and reduce time on customers with low lifetime value.

A bank might integrate the analytics platform alongside its existing enterprise sales systems. The customer service representatives in the bank can then use the dashboard to see the lifetime value for all their customers and prioritize the customers with longer lifetime value.

Process Automation

Process automation is one of the most common applications of machine learning in finance. The technology allows us to replace manual work, automate repetitive tasks, and increase productivity.

As a result, machine learning enables companies to optimize costs, improve customer experiences, and scale-up services. Here are automation use cases of machine learning in finance:

  • Chatbots
  • Call-center automation.
  • Paperwork automation.
  • Gamification of employee training, and more.

Below are some examples of process automation in banking:

JPMorgan Chase launched a Contract Intelligence (COiN) platform that leverages Natural Language Processing, one of the machine learning techniques. The solution processes legal documents and extracts essential data from them. Manual review of 12,000 annual commercial credit agreements would typically take up around 360,000 labor hours. Whereas, machine learning allows you to review the same number of contracts in just a few hours.

BNY Mello integrates process automation into their banking ecosystem. This innovation is responsible for $300,000 in annual savings and has brought about a wide range of operational improvements.

Wells Fargo uses an AI-driven chatbot through the Facebook Messenger platform to communicate with users and provide assistance with passwords and accounts.

Privatbank is a Ukrainian bank that implements chatbot assistants across its mobile and web platforms. Chatbots sped up the resolution of general customer queries and allowed the number of human assistants to decrease.


Robo-advisors are now commonplace in the financial domain. Currently, there are two major applications of machine learning in the advisory domain.

Portfolio management is an online wealth management service that uses algorithms and statistics to allocate, manage and optimize clients’ assets. Users enter their present financial assets and goals, say, saving a million dollars by the age of 50. A Robo-advisor then allocates the current assets across investment opportunities based on the risk preferences and the desired goals.

Recommendation of financial products. Many online insurance services use Robo-advisors to recommend personalized insurance plans to a particular user. Customers choose Robo-advisors over personal financial advisors due to lower fees, as well as personalized and calibrated recommendations.